574 research outputs found
Images of connected sets by semicontinuous multifunctions
AbstractThe image of a connected set by an upper-semicontinuous (or a lower-semicontinuous) multifunction whose values are nonempty and connected is connected. We prove this theorem in its most general setting and show its usefulness in various examples from optimization and nonlinear analysis
Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states
We propose a discrete transition-based reweighting analysis method (dTRAM)
for analyzing configuration-space-discretized simulation trajectories produced
at different thermodynamic states (temperatures, Hamiltonians, etc.) dTRAM
provides maximum-likelihood estimates of stationary quantities (probabilities,
free energies, expectation values) at any thermodynamic state. In contrast to
the weighted histogram analysis method (WHAM), dTRAM does not require data to
be sampled from global equilibrium, and can thus produce superior estimates for
enhanced sampling data such as parallel/simulated tempering, replica exchange,
umbrella sampling, or metadynamics. In addition, dTRAM provides optimal
estimates of Markov state models (MSMs) from the discretized state-space
trajectories at all thermodynamic states. Under suitable conditions, these MSMs
can be used to calculate kinetic quantities (e.g. rates, timescales). In the
limit of a single thermodynamic state, dTRAM estimates a maximum likelihood
reversible MSM, while in the limit of uncorrelated sampling data, dTRAM is
identical to WHAM. dTRAM is thus a generalization to both estimators
Permanently going back and forth between the âquadratic worldâ and the âconvexity worldâ in optimization
The objective of this work is twofold. Firstly, we propose a review of different results concerning convexity of images by quadratic mappings, putting them in a chronological perspective. Next, we enlighten these results from a geometrical point of view in order to provide new and comprehensive proofs and to immerse them in a more general and abstract context
A Fresh Variational-Analysis Look at the Positive Semidefinite Matrices World
International audienceEngineering sciences and applications of mathematics show unambiguously that positive semidefiniteness of matrices is the most important generalization of non-negative real num- bers. This notion of non-negativity for matrices has been well-studied in the literature; it has been the subject of review papers and entire chapters of books. This paper reviews some of the nice, useful properties of positive (semi)definite matrices, and insists in particular on (i) characterizations of positive (semi)definiteness and (ii) the geometrical properties of the set of positive semidefinite matrices. Some properties that turn out to be less well-known have here a special treatment. The use of these properties in optimization, as well as various references to applications, are spread all the way through. The "raison d'ĂȘtre" of this paper is essentially pedagogical; it adopts the viewpoint of variational analysis, shedding new light on the topic. Important, fruitful, and subtle, the positive semidefinite world is a good place to start with this domain of applied mathematics
When some variational properties force convexity
Abstract The notion of adequate (resp. strongly adequate) function has been recently introduced to characterize the essentially strictly convex (resp. essentially firmly subdifferentiable) functions among the weakly lower semicontinuous (resp. lower semicontinuous) ones. In this paper we provide various necessary and sufficient conditions in order that the lower semicontinuous hull of an extended real-valued function on a reflexive Banach space is essentially strictly convex. Some new results on nearest (farthest) points are derived from this approach. keywords Convex duality, well posed optimization problem, essential strict convexity, essential smoothness, best approximation
On representations of the feasible set in convex optimization
We consider the convex optimization problem where is convex, the feasible set K is convex and Slater's
condition holds, but the functions are not necessarily convex. We show
that for any representation of K that satisfies a mild nondegeneracy
assumption, every minimizer is a Karush-Kuhn-Tucker (KKT) point and conversely
every KKT point is a minimizer. That is, the KKT optimality conditions are
necessary and sufficient as in convex programming where one assumes that the
are convex. So in convex optimization, and as far as one is concerned
with KKT points, what really matters is the geometry of K and not so much its
representation.Comment: to appear in Optimization Letter
Local distinguishability of quantum states in infinite dimensional systems
We investigate local distinguishability of quantum states by use of the
convex analysis about joint numerical range of operators on a Hilbert space. We
show that any two orthogonal pure states are distinguishable by local
operations and classical communications, even for infinite dimensional systems.
An estimate of the local discrimination probability is also given for some
family of more than two pure states
Quasiconvex Constrained Multicriteria Continuous Location Problems: Structure of Nondominated Solution Sets
In this paper, we consider constrained multicriteria continuous location problems in two-dimensional spaces. In the literature, the continuous multicriteria location problem in two-dimensional spaces has received special attention in the last years, although only particular instances of convex functions have been considered. Our approach only requires the functions to be strictly quasiconvex and inf-compact. We obtain a geometrical description that provides a unified approach to handle multicriteria location models in two-dimensional spaces which has been implemented in MATHEMATIC
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